Advances in Machine Learning for Wetland Mapping and Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".
Deadline for manuscript submissions: 28 May 2026 | Viewed by 949
Special Issue Editor
Special Issue Information
Dear Colleagues,
Wetlands are among the most valuable ecosystems on the planet. They provide an abundance of critical services, supporting people and biodiversity. Wetlands buffer against floods, improve water quality, and are globally significant carbon sinks, sequestering huge amounts of carbon in their soils and vegetation. However, these unique habitats are under immense stress from human developments and climate change. The mapping and subsequent monitoring of wetlands is therefore an important endeavor for their conservation and sustainable management.
The application of remote sensing and Earth observation technologies has advanced wetland research by enabling detection and characterization across multiple spatial scales. Despite this progress, significant challenges remain, particularly in capturing the fine-scale complexities and hydrological regimes of wetlands, as well as the need for large and reliable training datasets. Emerging approaches using artificial intelligence (AI) and machine learning, including deep learning and neural networks, are beginning to address these limitations. These technological advancements hold great promise for improving wetland mapping and monitoring.
This Special Issue, in line with Remote Sensing’s emphasis on data and methodological innovations, focuses on recent advances in the remote sensing of wetland ecosystems, with particular attention to developments in AI and machine learning. We welcome studies addressing the following themes:
- Advances in AI and machine learning for wetland mapping and monitoring;
- Deep learning and neural networks for automated wetland detection, classification, and delineation;
- Data and sensor fusion for enhanced wetland characterization;
- Time-series analysis and change detection modeling of wetland dynamics;
- Transferability and scalability of AI models across regions and wetland types;
- Cloud-based machine learning platforms (e.g., Google Earth Engine) for wetland applications;
- Case studies showcasing innovative AI and machine learning applications in wetland remote sensing.
Dr. Michael Allan Merchant
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- deep learning
- earth observation
- machine learning
- remote sensing
- wetlands
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